code: def prepare(self, priming_data, max_dimensions=100):
      priming_data = self._clean_col_data(priming_data)
      self._binarizer.fit(priming_data + [("None")])
      for arr in priming_data:
          for x in arr:
              self._seen.add(x)
      self.is_prepared = True
      self.output_size = len(self.encode(priming_data[0:1])[0])
  
file_path: lightwood/lightwood/encoder/categorical/multihot.py
imports: import torch
  import numpy
  from lightwood.encoder import BaseEncoder
  from sklearn.preprocessing import MultiLabelBinarizer
label_desc: Use image node_id 3 for calling the MultiHotEncoder obj's underlying member method code with example usage: obj.prepare(priming_data, max_dimensions) without return types